The present invention relates generally to methods and circuits for suppressing radar output due to clutter while retaining desired output due to targets that are moving with velocities different than those of clutter. More specifically, the present invention relates to radar doppler filters using adaptive filtering and a fast orthogonalization network.
The detection of moving targets by a radar system is often limited by echoes from very large stationary objects, as well as by receiver noise. The returns from stationary objects, referred to as clutter, are usually discriminated from returns from moving targets through velocity filtering techniques designed to maximize signal-to-clutter ratio (SCR). The effects of receiver noise are usually reduced through the use of coherent or noncoherent integration designed to maximize signal-to-noise ratio (SNR). To maximize the detectability of moving targets, the radar should be designed to reject simultaneously clutter plus noise, that is to maximize the signal-to-interference ratio (SIR) when the total interference consists of clutter plus noise.
Adaptive filtering can be applied to radar doppler filter design. Doppler filters are designed to accept doppler frequency shifted moving targets while rejecting the returns from the target background (clutter). The clutter is usually slow moving so that its energy is normally concentrated about the zero doppler frequency. A bank of filters is used to cover the entire doppler band; i.e. the doppler band is equally divided into subbands. Ideally, it would be desirable to place a rectangular bandpass filter about each subband so that the large clutter return is completely rejected out of band. However, only approximations of this rectangular filter are realizable. It has been shown that adaptive doppler processing yields superior signal-to-clutter power ratio improvement performance over these approximate rectangular filter implementations. This results because each doppler filter is designed not only to accept a desired signal but also to place nulls at frequencies out of band where clutter returns exist. Each doppler filter is optimized with respect to the doppler filter's allocated subband using an adaptive algorithm.
If the radar clutter spectrum is known apriori, then it is possible to design the optimum doppler filters apriori. However, in some cases, for instance radar rain clutter, it may be necessary to adapt these doppler filters to the dynamic radar clutter. Thus, the radar clutter spectrum is estimated on line and this knowledge is used to develop the optimal weighting on each of the doppler filters.
The direct adaptive filtering of multiple input channels by Gram-Schmidt orthogonalization has been the subject of intense research during the past decade. The Gram-Schmidt technique (sometimes called the Adaptive Lattice Filter) has been shown to yield superior performance simultaneously in arithmetic efficiency, stability, and convergence times over other adaptive algorithms.
In adaptive filtering, it is desirable to find the optimal weighting of multiple input channels such that the output signal to noise power ratio (S/N) is a maximum. The desired signal is associated with a desired signal column vector, s, where s=(s.sub.1,s.sub.2 . . . , s.sub.n).sup.T, N is the number of input channels, and T denotes the Vector transpose. The vector component, s.sub.n,n=1,2 . . . , N represents the desired signal's component in the nth input channel. If w is an N-length column vector denoting the optimal weighting of the N input channels and x is an N-length column vector denoting the data from the N input channels, then it can be shown that w must satisfy the following vector equation: EQU R.sub.xx w=.mu.s*, (1)
where EQU R.sub.xx =E(x*x.sup.T), (2)
.mu. is an arbitrary constant which for convenience we set equal to one, E(.multidot.) denotes the expected value, and * denotes the complex conjugate. Equation 1 is often referred to as the Applebaum Adaptive Algorithm. The matrix, R.sub.xx, is called the input covariance matrix.
For some filtering applications, there may be as many output channels as there are input channels (such as a doppler processor). Hence, there will be N desired signal vectors. Defining S to be the N.times.N steering matrix of desired signal vectors; i.e., EQU S=(s.sub.1, s.sub.2, . . . , s.sub.N) (3)
where s.sub.n, n=1, 2, . . . ,N are column vectors of the desirable signals. If W is defined as the optimal N.times.N weighting matrix, i.e., the weights that optimize the S/N in each of the output channels, then these weights satisfy the following matrix equation EQU R.sub.xx W=S*. (4)
Problems occur in the solution for the weights if R.sub.xx is ill conditioned. Due to computational inaccuracies, the algorithm can become unstable and the output channels extremely noisy.